Path to a free self-taught education in Computer Science!
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Science

References

Method

Here we'll try to choose a maximum of 3 courses by category. After finished all courses, more categories and/or courses will be added to this list.

Initially, we will also give preference for MOOC (Massive Open Online Course) type of courses because those courses were created with our style of learning in mind.

Tips

If you want to follow this path, here are some tips! Share your tips with us too!

Topics

Course Duration Files Status
weeks -

Introduction

Course Duration Files Status
Introduction to Computer Science 9 ~ 15 weeks -
Introduction to Computer Science and Programming Using Python 9 weeks -
Introduction to Computational Thinking and Data Science 10 weeks -

Program Design

  1. Systematic Program Design- Part 1: The Core Method -
  2. Systematic Program Design- Part 2: Arbitrary Sized Data -
  3. Systematic Program Design- Part 3: Abstraction, Search and Graphs -

Programming Paradigms

Software Testing

  1. Software Testing -
  2. Software Debugging -

Math

  1. Mathematics for Computer Science -
  2. Introduction to Logic -
  3. Linear Algebra -
  4. Coding the Matrix: Linear Algebra through Computer Science Applications -
  5. Calculus One -
  6. Calculus Two -
  7. Linear and Discrete Optimization -
  8. Probabilistic Graphical Models -
  9. Game Theory -
  10. Statistics One -
  11. AP Statistics -

Operating Systems

  1. Operating System Engineering -
  2. Operating Systems and System Programming -

Networks

  1. Networks -
  2. Network and Computer Security -
  3. Network Optimization -

Databases

  1. Database Systems -
  2. Database, Internet, and Systems Integration Technologies -

Cryptography

  1. Cryptography I -
  2. Applied Cryptography -

Compilers

  1. Compilers -

Artificial Intelligence

  1. Artificial Intelligence -

Machine Learning

  1. Practical Machine Learning -
  2. Machine Learning -
  3. Neural Networks for Machine Learning -

Natural Language Processing

  1. Natural Language Processing -
  2. Natural Language Processing -

Robotics

Graphs

Data Mining

  1. Data Mining -

Parallel Programming

  1. Parallel Computing -
  2. Heterogeneous Parallel Programming -

Programming Languages

  1. Practical Programming in C -
  2. Introduction to C Memory Management and C++ Object-Oriented Programming -
  3. Effective Programming in C and C++ -

Others

  1. Introduction to Functional Programming
  2. Engineering Software as a Service
  3. Engineering Software as a Service, Part 2
  4. Automata, Computability, and Complexity -
  5. Computational Biology: Genomes, Networks, Evolution -
  6. Creating Video Games -
  7. Computer Graphics -
  8. User Interface Design and Implementation -
  9. Making Sense of Data -
  10. Data Science -